Overview

Dataset statistics

Number of variables24
Number of observations1156772
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory207.4 MiB
Average record size in memory188.0 B

Variable types

Numeric17
Categorical7

Warnings

addr_state has a high cardinality: 51 distinct values High cardinality
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 1 other fieldsHigh correlation
loan_status is highly correlated with ROIHigh correlation
open_acc is highly correlated with total_accHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 2 other fieldsHigh correlation
ROI is highly correlated with loan_status and 1 other fieldsHigh correlation
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 1 other fieldsHigh correlation
loan_status is highly correlated with ROIHigh correlation
open_acc is highly correlated with total_accHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 2 other fieldsHigh correlation
ROI is highly correlated with loan_status and 1 other fieldsHigh correlation
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 1 other fieldsHigh correlation
loan_status is highly correlated with ROIHigh correlation
open_acc is highly correlated with total_accHigh correlation
total_acc is highly correlated with open_accHigh correlation
total_pymnt is highly correlated with loan_amnt and 2 other fieldsHigh correlation
ROI is highly correlated with loan_status and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
loan_status is highly correlated with ROIHigh correlation
term is highly correlated with ROI and 1 other fieldsHigh correlation
total_pymnt is highly correlated with installment and 2 other fieldsHigh correlation
installment is highly correlated with total_pymnt and 2 other fieldsHigh correlation
total_acc is highly correlated with open_accHigh correlation
ROI is highly correlated with loan_status and 4 other fieldsHigh correlation
loan_amnt is highly correlated with term and 3 other fieldsHigh correlation
annual_inc is highly skewed (γ1 = 47.28305342) Skewed
emp_length has 98508 (8.5%) zeros Zeros
delinq_2yrs has 930540 (80.4%) zeros Zeros
inq_last_6mths has 661742 (57.2%) zeros Zeros
pub_rec has 961673 (83.1%) zeros Zeros

Reproduction

Analysis started2021-07-15 03:26:13.364833
Analysis finished2021-07-15 03:31:51.891973
Duration5 minutes and 38.53 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1549
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14294.20828
Minimum500
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:52.081789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3175
Q17800
median12000
Q320000
95-th percentile32225
Maximum40000
Range39500
Interquartile range (IQR)12200

Descriptive statistics

Standard deviation8677.84587
Coefficient of variation (CV)0.6070882486
Kurtosis-0.02323530406
Mean14294.20828
Median Absolute Deviation (MAD)5825
Skewness0.8065039352
Sum1.65351399 × 1010
Variance75305008.94
MonotonicityNot monotonic
2021-07-14T22:31:52.248116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000086761
 
7.5%
1200064025
 
5.5%
1500060165
 
5.2%
2000059788
 
5.2%
500042559
 
3.7%
3500042349
 
3.7%
800042230
 
3.7%
600039661
 
3.4%
1600031853
 
2.8%
2500028528
 
2.5%
Other values (1539)658853
57.0%
ValueCountFrequency (%)
5002
 
< 0.1%
8001
 
< 0.1%
9501
 
< 0.1%
10005004
0.4%
102514
 
< 0.1%
105035
 
< 0.1%
107516
 
< 0.1%
1100130
 
< 0.1%
112528
 
< 0.1%
115027
 
< 0.1%
ValueCountFrequency (%)
400006115
0.5%
399754
 
< 0.1%
399502
 
< 0.1%
399006
 
< 0.1%
398751
 
< 0.1%
398502
 
< 0.1%
398253
 
< 0.1%
398003
 
< 0.1%
397755
 
< 0.1%
397503
 
< 0.1%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
36 months
879067 
60 months
277705 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11567720
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 60 months
4th row 60 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months879067
76.0%
60 months277705
 
24.0%

Length

2021-07-14T22:31:52.491783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-14T22:31:52.554268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
months1156772
50.0%
36879067
38.0%
60277705
 
12.0%

Most occurring characters

ValueCountFrequency (%)
2313544
20.0%
61156772
10.0%
m1156772
10.0%
o1156772
10.0%
n1156772
10.0%
t1156772
10.0%
h1156772
10.0%
s1156772
10.0%
3879067
 
7.6%
0277705
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6940632
60.0%
Space Separator2313544
 
20.0%
Decimal Number2313544
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m1156772
16.7%
o1156772
16.7%
n1156772
16.7%
t1156772
16.7%
h1156772
16.7%
s1156772
16.7%
Decimal Number
ValueCountFrequency (%)
61156772
50.0%
3879067
38.0%
0277705
 
12.0%
Space Separator
ValueCountFrequency (%)
2313544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6940632
60.0%
Common4627088
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m1156772
16.7%
o1156772
16.7%
n1156772
16.7%
t1156772
16.7%
h1156772
16.7%
s1156772
16.7%
Common
ValueCountFrequency (%)
2313544
50.0%
61156772
25.0%
3879067
 
19.0%
0277705
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11567720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2313544
20.0%
61156772
10.0%
m1156772
10.0%
o1156772
10.0%
n1156772
10.0%
t1156772
10.0%
h1156772
10.0%
s1156772
10.0%
3879067
 
7.6%
0277705
 
2.4%

installment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78622
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.0155144
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:52.649452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile107.95
Q1246.99
median371.07
Q3573.32
95-th percentile956.35
Maximum1719.83
Range1714.9
Interquartile range (IQR)326.33

Descriptive statistics

Standard deviation259.5520317
Coefficient of variation (CV)0.5980247782
Kurtosis0.8086749356
Mean434.0155144
Median Absolute Deviation (MAD)153.13
Skewness1.024974036
Sum502056994.6
Variance67367.25718
MonotonicityNot monotonic
2021-07-14T22:31:52.781341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
327.342683
 
0.2%
301.152443
 
0.2%
332.12325
 
0.2%
318.792018
 
0.2%
312.861896
 
0.2%
361.381842
 
0.2%
451.731762
 
0.2%
392.811757
 
0.2%
602.31750
 
0.2%
491.011696
 
0.1%
Other values (78612)1136600
98.3%
ValueCountFrequency (%)
4.931
< 0.1%
14.011
< 0.1%
14.771
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
19.871
< 0.1%
20.221
< 0.1%
21.251
< 0.1%
21.621
< 0.1%
21.741
< 0.1%
ValueCountFrequency (%)
1719.831
 
< 0.1%
1717.631
 
< 0.1%
1715.421
 
< 0.1%
1618.031
 
< 0.1%
1607.82
 
< 0.1%
1587.232
 
< 0.1%
1584.91
 
< 0.1%
1569.113
< 0.1%
1568.251
 
< 0.1%
1566.87
< 0.1%

grade
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.729148873
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:52.897315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.280707666
Coefficient of variation (CV)0.4692699906
Kurtosis0.1377284728
Mean2.729148873
Median Absolute Deviation (MAD)1
Skewness0.6303438821
Sum3157003
Variance1.640212126
MonotonicityNot monotonic
2021-07-14T22:31:52.991012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3336447
29.1%
2334062
28.9%
1205333
17.8%
4170523
14.7%
577467
 
6.7%
625802
 
2.2%
77138
 
0.6%
ValueCountFrequency (%)
1205333
17.8%
2334062
28.9%
3336447
29.1%
4170523
14.7%
577467
 
6.7%
625802
 
2.2%
77138
 
0.6%
ValueCountFrequency (%)
77138
 
0.6%
625802
 
2.2%
577467
 
6.7%
4170523
14.7%
3336447
29.1%
2334062
28.9%
1205333
17.8%

emp_length
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.967095504
Minimum0
Maximum10
Zeros98508
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:53.084772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.693057306
Coefficient of variation (CV)0.6189036699
Kurtosis-1.500871438
Mean5.967095504
Median Absolute Deviation (MAD)4
Skewness-0.21735933
Sum6902569
Variance13.63867227
MonotonicityNot monotonic
2021-07-14T22:31:53.206691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10404499
35.0%
2111509
 
9.6%
098508
 
8.5%
398359
 
8.5%
181044
 
7.0%
576506
 
6.6%
473325
 
6.3%
657018
 
4.9%
855380
 
4.8%
754077
 
4.7%
ValueCountFrequency (%)
098508
8.5%
181044
7.0%
2111509
9.6%
398359
8.5%
473325
6.3%
576506
6.6%
657018
4.9%
754077
4.7%
855380
4.8%
946547
4.0%
ValueCountFrequency (%)
10404499
35.0%
946547
 
4.0%
855380
 
4.8%
754077
 
4.7%
657018
 
4.9%
576506
 
6.6%
473325
 
6.3%
398359
 
8.5%
2111509
 
9.6%
181044
 
7.0%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
MORTGAGE
571621 
RENT
465503 
OWN
119302 
ANY
 
259
OTHER
 
48

Length

Max length8
Median length4
Mean length5.873291366
Min length3

Characters and Unicode

Total characters6794059
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE571621
49.4%
RENT465503
40.2%
OWN119302
 
10.3%
ANY259
 
< 0.1%
OTHER48
 
< 0.1%
NONE39
 
< 0.1%

Length

2021-07-14T22:31:53.442264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-14T22:31:53.516876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage571621
49.4%
rent465503
40.2%
own119302
 
10.3%
any259
 
< 0.1%
other48
 
< 0.1%
none39
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G1143242
16.8%
E1037211
15.3%
R1037172
15.3%
T1037172
15.3%
O691010
10.2%
N585142
8.6%
A571880
8.4%
M571621
8.4%
W119302
 
1.8%
Y259
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6794059
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G1143242
16.8%
E1037211
15.3%
R1037172
15.3%
T1037172
15.3%
O691010
10.2%
N585142
8.6%
A571880
8.4%
M571621
8.4%
W119302
 
1.8%
Y259
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin6794059
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1143242
16.8%
E1037211
15.3%
R1037172
15.3%
T1037172
15.3%
O691010
10.2%
N585142
8.6%
A571880
8.4%
M571621
8.4%
W119302
 
1.8%
Y259
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6794059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1143242
16.8%
E1037211
15.3%
R1037172
15.3%
T1037172
15.3%
O691010
10.2%
N585142
8.6%
A571880
8.4%
M571621
8.4%
W119302
 
1.8%
Y259
 
< 0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct52633
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77545.7614
Minimum33
Maximum10999200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:53.631665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile30000
Q147000
median65000
Q392000
95-th percentile158700
Maximum10999200
Range10999167
Interquartile range (IQR)45000

Descriptive statistics

Standard deviation71428.22134
Coefficient of variation (CV)0.921110581
Kurtosis4844.268239
Mean77545.7614
Median Absolute Deviation (MAD)21000
Skewness47.28305342
Sum8.970276551 × 1010
Variance5101990803
MonotonicityNot monotonic
2021-07-14T22:31:53.783135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000045123
 
3.9%
5000040125
 
3.5%
6500034174
 
3.0%
7000032572
 
2.8%
4000031300
 
2.7%
8000031156
 
2.7%
7500030473
 
2.6%
4500029307
 
2.5%
5500027690
 
2.4%
10000023956
 
2.1%
Other values (52623)830896
71.8%
ValueCountFrequency (%)
331
 
< 0.1%
1003
< 0.1%
4871
 
< 0.1%
6001
 
< 0.1%
7501
 
< 0.1%
8001
 
< 0.1%
10004
< 0.1%
10661
 
< 0.1%
12001
 
< 0.1%
14001
 
< 0.1%
ValueCountFrequency (%)
109992001
< 0.1%
95500001
< 0.1%
95229721
< 0.1%
95000001
< 0.1%
93000001
< 0.1%
92250001
< 0.1%
90000001
< 0.1%
89000601
< 0.1%
87065821
< 0.1%
87000001
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
Source Verified
469665 
Not Verified
362340 
Verified
324767 

Length

Max length15
Median length12
Mean length12.0950291
Min length8

Characters and Unicode

Total characters13991191
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Source Verified469665
40.6%
Not Verified362340
31.3%
Verified324767
28.1%

Length

2021-07-14T22:31:54.046951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-14T22:31:54.114920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
verified1156772
58.2%
source469665
23.6%
not362340
 
18.2%

Most occurring characters

ValueCountFrequency (%)
e2783209
19.9%
i2313544
16.5%
r1626437
11.6%
V1156772
8.3%
f1156772
8.3%
d1156772
8.3%
o832005
 
5.9%
832005
 
5.9%
S469665
 
3.4%
u469665
 
3.4%
Other values (3)1194345
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11170409
79.8%
Uppercase Letter1988777
 
14.2%
Space Separator832005
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2783209
24.9%
i2313544
20.7%
r1626437
14.6%
f1156772
10.4%
d1156772
10.4%
o832005
 
7.4%
u469665
 
4.2%
c469665
 
4.2%
t362340
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
V1156772
58.2%
S469665
23.6%
N362340
 
18.2%
Space Separator
ValueCountFrequency (%)
832005
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13159186
94.1%
Common832005
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2783209
21.2%
i2313544
17.6%
r1626437
12.4%
V1156772
8.8%
f1156772
8.8%
d1156772
8.8%
o832005
 
6.3%
S469665
 
3.6%
u469665
 
3.6%
c469665
 
3.6%
Other values (2)724680
 
5.5%
Common
ValueCountFrequency (%)
832005
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13991191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2783209
19.9%
i2313544
16.5%
r1626437
11.6%
V1156772
8.3%
f1156772
8.3%
d1156772
8.3%
o832005
 
5.9%
832005
 
5.9%
S469665
 
3.4%
u469665
 
3.4%
Other values (3)1194345
8.5%

loan_status
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
1
931992 
0
224780 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1156772
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

Length

2021-07-14T22:31:54.329972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-14T22:31:54.398178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

Most occurring characters

ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1156772
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
Common1156772
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1156772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1931992
80.6%
0224780
 
19.4%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
debt_consolidation
670735 
credit_card
254847 
home_improvement
74334 
other
67747 
major_purchase
 
25311
Other values (9)
 
63798

Length

Max length18
Median length18
Mean length14.90281404
Min length3

Characters and Unicode

Total characters17239158
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowsmall_business
3rd rowhome_improvement
4th rowmajor_purchase
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation670735
58.0%
credit_card254847
 
22.0%
home_improvement74334
 
6.4%
other67747
 
5.9%
major_purchase25311
 
2.2%
medical13441
 
1.2%
small_business12716
 
1.1%
car12587
 
1.1%
moving8437
 
0.7%
vacation7959
 
0.7%
Other values (4)8658
 
0.7%

Length

2021-07-14T22:31:54.614808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation670735
58.0%
credit_card254847
 
22.0%
home_improvement74334
 
6.4%
other67747
 
5.9%
major_purchase25311
 
2.2%
medical13441
 
1.2%
small_business12716
 
1.1%
car12587
 
1.1%
moving8437
 
0.7%
vacation7959
 
0.7%
Other values (4)8658
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o2276539
13.2%
d1867988
10.8%
t1746456
10.1%
i1714945
9.9%
n1448265
8.4%
e1279673
7.4%
c1239826
7.2%
_1038747
 
6.0%
a1031868
 
6.0%
s753023
 
4.4%
Other values (12)2841828
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16200411
94.0%
Connector Punctuation1038747
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2276539
14.1%
d1867988
11.5%
t1746456
10.8%
i1714945
10.6%
n1448265
8.9%
e1279673
7.9%
c1239826
7.7%
a1031868
6.4%
s753023
 
4.6%
r716592
 
4.4%
Other values (11)2125236
13.1%
Connector Punctuation
ValueCountFrequency (%)
_1038747
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16200411
94.0%
Common1038747
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o2276539
14.1%
d1867988
11.5%
t1746456
10.8%
i1714945
10.6%
n1448265
8.9%
e1279673
7.9%
c1239826
7.7%
a1031868
6.4%
s753023
 
4.6%
r716592
 
4.4%
Other values (11)2125236
13.1%
Common
ValueCountFrequency (%)
_1038747
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17239158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o2276539
13.2%
d1867988
10.8%
t1746456
10.1%
i1714945
9.9%
n1448265
8.4%
e1279673
7.4%
c1239826
7.2%
_1038747
 
6.0%
a1031868
 
6.0%
s753023
 
4.4%
Other values (12)2841828
16.5%

addr_state
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
CA
168991 
TX
95967 
NY
94282 
FL
80695 
IL
 
44856
Other values (46)
671981 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2313544
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPA
2nd rowSD
3rd rowIL
4th rowPA
5th rowGA

Common Values

ValueCountFrequency (%)
CA168991
 
14.6%
TX95967
 
8.3%
NY94282
 
8.2%
FL80695
 
7.0%
IL44856
 
3.9%
NJ41924
 
3.6%
PA39288
 
3.4%
OH37679
 
3.3%
GA37077
 
3.2%
VA32916
 
2.8%
Other values (41)483097
41.8%

Length

2021-07-14T22:31:54.898156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca168991
 
14.6%
tx95967
 
8.3%
ny94282
 
8.2%
fl80695
 
7.0%
il44856
 
3.9%
nj41924
 
3.6%
pa39288
 
3.4%
oh37679
 
3.3%
ga37077
 
3.2%
va32916
 
2.8%
Other values (41)483097
41.8%

Most occurring characters

ValueCountFrequency (%)
A396744
17.1%
C260642
11.3%
N259758
11.2%
L152964
 
6.6%
T145003
 
6.3%
M139771
 
6.0%
I121311
 
5.2%
Y107825
 
4.7%
O106092
 
4.6%
X95967
 
4.1%
Other values (14)527467
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2313544
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A396744
17.1%
C260642
11.3%
N259758
11.2%
L152964
 
6.6%
T145003
 
6.3%
M139771
 
6.0%
I121311
 
5.2%
Y107825
 
4.7%
O106092
 
4.6%
X95967
 
4.1%
Other values (14)527467
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2313544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A396744
17.1%
C260642
11.3%
N259758
11.2%
L152964
 
6.6%
T145003
 
6.3%
M139771
 
6.0%
I121311
 
5.2%
Y107825
 
4.7%
O106092
 
4.6%
X95967
 
4.1%
Other values (14)527467
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2313544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A396744
17.1%
C260642
11.3%
N259758
11.2%
L152964
 
6.6%
T145003
 
6.3%
M139771
 
6.0%
I121311
 
5.2%
Y107825
 
4.7%
O106092
 
4.6%
X95967
 
4.1%
Other values (14)527467
22.8%

dti
Real number (ℝ)

Distinct6379
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.14050433
Minimum-1
Maximum999
Zeros590
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size8.8 MiB
2021-07-14T22:31:55.007509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5.01
Q111.76
median17.53
Q323.93
95-th percentile32.78
Maximum999
Range1000
Interquartile range (IQR)12.17

Descriptive statistics

Standard deviation9.615304137
Coefficient of variation (CV)0.5300461313
Kurtosis1231.736228
Mean18.14050433
Median Absolute Deviation (MAD)6.06
Skewness14.65419916
Sum20984427.47
Variance92.45407365
MonotonicityNot monotonic
2021-07-14T22:31:55.165053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2864
 
0.1%
18850
 
0.1%
16.8838
 
0.1%
14.4837
 
0.1%
15.6792
 
0.1%
13.2777
 
0.1%
12762
 
0.1%
20.4745
 
0.1%
21.6727
 
0.1%
10.8693
 
0.1%
Other values (6369)1148887
99.3%
ValueCountFrequency (%)
-12
 
< 0.1%
0590
0.1%
0.019
 
< 0.1%
0.0215
 
< 0.1%
0.0310
 
< 0.1%
0.047
 
< 0.1%
0.059
 
< 0.1%
0.0621
 
< 0.1%
0.0716
 
< 0.1%
0.0813
 
< 0.1%
ValueCountFrequency (%)
99910
< 0.1%
831.971
 
< 0.1%
797.11
 
< 0.1%
771.311
 
< 0.1%
762.51
 
< 0.1%
669.231
 
< 0.1%
606.81
 
< 0.1%
602.671
 
< 0.1%
592.121
 
< 0.1%
586.791
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3242229238
Minimum0
Maximum39
Zeros930540
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:55.281634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8912197426
Coefficient of variation (CV)2.748786952
Kurtosis58.92139786
Mean0.3242229238
Median Absolute Deviation (MAD)0
Skewness5.609803477
Sum375052
Variance0.7942726296
MonotonicityNot monotonic
2021-07-14T22:31:55.414999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0930540
80.4%
1149880
 
13.0%
244085
 
3.8%
316116
 
1.4%
47167
 
0.6%
53718
 
0.3%
62061
 
0.2%
71150
 
0.1%
8681
 
0.1%
9421
 
< 0.1%
Other values (21)953
 
0.1%
ValueCountFrequency (%)
0930540
80.4%
1149880
 
13.0%
244085
 
3.8%
316116
 
1.4%
47167
 
0.6%
53718
 
0.3%
62061
 
0.2%
71150
 
0.1%
8681
 
0.1%
9421
 
< 0.1%
ValueCountFrequency (%)
391
 
< 0.1%
301
 
< 0.1%
292
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
263
< 0.1%
252
< 0.1%
241
 
< 0.1%
223
< 0.1%
214
< 0.1%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6521492567
Minimum0
Maximum8
Zeros661742
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:55.536606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9316926401
Coefficient of variation (CV)1.428649394
Kurtosis3.278905143
Mean0.6521492567
Median Absolute Deviation (MAD)0
Skewness1.69294849
Sum754388
Variance0.8680511757
MonotonicityNot monotonic
2021-07-14T22:31:55.631534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0661742
57.2%
1318057
27.5%
2117027
 
10.1%
343281
 
3.7%
411738
 
1.0%
54125
 
0.4%
6770
 
0.1%
719
 
< 0.1%
813
 
< 0.1%
ValueCountFrequency (%)
0661742
57.2%
1318057
27.5%
2117027
 
10.1%
343281
 
3.7%
411738
 
1.0%
54125
 
0.4%
6770
 
0.1%
719
 
< 0.1%
813
 
< 0.1%
ValueCountFrequency (%)
813
 
< 0.1%
719
 
< 0.1%
6770
 
0.1%
54125
 
0.4%
411738
 
1.0%
343281
 
3.7%
2117027
 
10.1%
1318057
27.5%
0661742
57.2%

open_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct83
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.68118696
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:55.763661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median11
Q314
95-th percentile22
Maximum90
Range89
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5042328
Coefficient of variation (CV)0.4712049229
Kurtosis3.384592643
Mean11.68118696
Median Absolute Deviation (MAD)3
Skewness1.302245563
Sum13512470
Variance30.29657872
MonotonicityNot monotonic
2021-07-14T22:31:55.899888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9102226
 
8.8%
1099687
 
8.6%
897897
 
8.5%
1192525
 
8.0%
787558
 
7.6%
1283287
 
7.2%
1372691
 
6.3%
672297
 
6.2%
1462109
 
5.4%
1552001
 
4.5%
Other values (73)334494
28.9%
ValueCountFrequency (%)
1350
 
< 0.1%
23752
 
0.3%
313291
 
1.1%
431001
 
2.7%
551874
4.5%
672297
6.2%
787558
7.6%
897897
8.5%
9102226
8.8%
1099687
8.6%
ValueCountFrequency (%)
901
 
< 0.1%
881
 
< 0.1%
861
 
< 0.1%
841
 
< 0.1%
821
 
< 0.1%
811
 
< 0.1%
802
< 0.1%
791
 
< 0.1%
771
 
< 0.1%
763
< 0.1%

pub_rec
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21453493
Minimum0
Maximum63
Zeros961673
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:56.031576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5975280812
Coefficient of variation (CV)2.785225144
Kurtosis456.647265
Mean0.21453493
Median Absolute Deviation (MAD)0
Skewness9.729955051
Sum248168
Variance0.3570398078
MonotonicityNot monotonic
2021-07-14T22:31:56.143081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0961673
83.1%
1163132
 
14.1%
221069
 
1.8%
36371
 
0.6%
42279
 
0.2%
51087
 
0.1%
6548
 
< 0.1%
7241
 
< 0.1%
8130
 
< 0.1%
968
 
< 0.1%
Other values (25)174
 
< 0.1%
ValueCountFrequency (%)
0961673
83.1%
1163132
 
14.1%
221069
 
1.8%
36371
 
0.6%
42279
 
0.2%
51087
 
0.1%
6548
 
< 0.1%
7241
 
< 0.1%
8130
 
< 0.1%
968
 
< 0.1%
ValueCountFrequency (%)
631
 
< 0.1%
611
 
< 0.1%
492
< 0.1%
471
 
< 0.1%
461
 
< 0.1%
401
 
< 0.1%
371
 
< 0.1%
341
 
< 0.1%
283
< 0.1%
251
 
< 0.1%

revol_bal
Real number (ℝ≥0)

Distinct79704
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16182.97523
Minimum0
Maximum2904836
Zeros4756
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:56.283641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1707
Q15945
median11073
Q319593
95-th percentile43269
Maximum2904836
Range2904836
Interquartile range (IQR)13648

Descriptive statistics

Standard deviation22347.68219
Coefficient of variation (CV)1.380937799
Kurtosis778.7699151
Mean16182.97523
Median Absolute Deviation (MAD)6090
Skewness14.17206609
Sum1.872001262 × 1010
Variance499418899.1
MonotonicityNot monotonic
2021-07-14T22:31:56.414654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04756
 
0.4%
523596
 
< 0.1%
631289
 
< 0.1%
888
 
< 0.1%
623987
 
< 0.1%
545387
 
< 0.1%
287
 
< 0.1%
524986
 
< 0.1%
531486
 
< 0.1%
611885
 
< 0.1%
Other values (79694)1151225
99.5%
ValueCountFrequency (%)
04756
0.4%
153
 
< 0.1%
287
 
< 0.1%
374
 
< 0.1%
460
 
< 0.1%
561
 
< 0.1%
675
 
< 0.1%
762
 
< 0.1%
888
 
< 0.1%
963
 
< 0.1%
ValueCountFrequency (%)
29048361
< 0.1%
25689951
< 0.1%
25607031
< 0.1%
17467161
< 0.1%
16967961
< 0.1%
12987831
< 0.1%
11900461
< 0.1%
10876641
< 0.1%
10442101
< 0.1%
10438601
< 0.1%

revol_util
Real number (ℝ≥0)

Distinct1325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.77143175
Minimum0
Maximum892.3
Zeros5672
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:56.564952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.6
Q133.4
median52
Q370.6
95-th percentile91.5
Maximum892.3
Range892.3
Interquartile range (IQR)37.2

Descriptive statistics

Standard deviation24.47262235
Coefficient of variation (CV)0.4727051487
Kurtosis0.4238643853
Mean51.77143175
Median Absolute Deviation (MAD)18.6
Skewness-0.022792492
Sum59887742.65
Variance598.9092445
MonotonicityNot monotonic
2021-07-14T22:31:56.698359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05672
 
0.5%
532294
 
0.2%
572279
 
0.2%
542271
 
0.2%
482266
 
0.2%
582258
 
0.2%
592236
 
0.2%
522224
 
0.2%
612210
 
0.2%
562205
 
0.2%
Other values (1315)1130857
97.8%
ValueCountFrequency (%)
05672
0.5%
0.031
 
< 0.1%
0.051
 
< 0.1%
0.1783
 
0.1%
0.121
 
< 0.1%
0.161
 
< 0.1%
0.2624
 
0.1%
0.3593
 
0.1%
0.4514
 
< 0.1%
0.461
 
< 0.1%
ValueCountFrequency (%)
892.31
< 0.1%
366.61
< 0.1%
1931
< 0.1%
184.61
< 0.1%
182.81
< 0.1%
177.71
< 0.1%
1721
< 0.1%
165.81
< 0.1%
1621
< 0.1%
156.31
< 0.1%

total_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.01253229
Minimum2
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:56.838990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q116
median23
Q332
95-th percentile47
Maximum176
Range174
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.02461624
Coefficient of variation (CV)0.4807436567
Kurtosis1.697894815
Mean25.01253229
Median Absolute Deviation (MAD)8
Skewness0.9647115528
Sum28933797
Variance144.5913956
MonotonicityNot monotonic
2021-07-14T22:31:56.981390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2041859
 
3.6%
2141472
 
3.6%
1941471
 
3.6%
2241378
 
3.6%
1840875
 
3.5%
2340714
 
3.5%
1740575
 
3.5%
2439632
 
3.4%
1638831
 
3.4%
2538202
 
3.3%
Other values (130)751763
65.0%
ValueCountFrequency (%)
2280
 
< 0.1%
31099
 
0.1%
44105
 
0.4%
56725
 
0.6%
69878
 
0.9%
713320
1.2%
816879
1.5%
920559
1.8%
1024130
2.1%
1127527
2.4%
ValueCountFrequency (%)
1761
< 0.1%
1691
< 0.1%
1561
< 0.1%
1512
< 0.1%
1501
< 0.1%
1462
< 0.1%
1442
< 0.1%
1411
< 0.1%
1401
< 0.1%
1381
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1087461
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14693.52947
Minimum0
Maximum63296.87792
Zeros680
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:57.131667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2510.4665
Q16817.02
median12025.62
Q320303.38367
95-th percentile36153.77547
Maximum63296.87792
Range63296.87792
Interquartile range (IQR)13486.36367

Descriptive statistics

Standard deviation10241.98898
Coefficient of variation (CV)0.6970407621
Kurtosis0.7199555622
Mean14693.52947
Median Absolute Deviation (MAD)6054.549034
Skewness1.057335482
Sum1.699706348 × 1010
Variance104898338.3
MonotonicityNot monotonic
2021-07-14T22:31:57.286999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0680
 
0.1%
10838.35484174
 
< 0.1%
11258.43637167
 
< 0.1%
13510.12859151
 
< 0.1%
16257.52756151
 
< 0.1%
16887.64456126
 
< 0.1%
13006.0278123
 
< 0.1%
27095.91178118
 
< 0.1%
22516.86275116
 
< 0.1%
21676.71967109
 
< 0.1%
Other values (1087451)1154857
99.8%
ValueCountFrequency (%)
0680
0.1%
101
 
< 0.1%
16.581
 
< 0.1%
17.581
 
< 0.1%
253
 
< 0.1%
301
 
< 0.1%
30.861
 
< 0.1%
31.331
 
< 0.1%
32.361
 
< 0.1%
33.631
 
< 0.1%
ValueCountFrequency (%)
63296.877921
< 0.1%
62862.506731
< 0.1%
62837.639691
< 0.1%
62760.913881
< 0.1%
62687.030891
< 0.1%
62686.388171
< 0.1%
62670.382321
< 0.1%
62663.618341
< 0.1%
62663.61831
< 0.1%
62451.008161
< 0.1%

application_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
Individual
1135954 
Joint App
 
20818

Length

Max length10
Median length10
Mean length9.982003368
Min length9

Characters and Unicode

Total characters11546902
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowJoint App
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual1135954
98.2%
Joint App20818
 
1.8%

Length

2021-07-14T22:31:57.515018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-14T22:31:57.581649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
individual1135954
96.5%
joint20818
 
1.8%
app20818
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i2292726
19.9%
d2271908
19.7%
n1156772
10.0%
I1135954
9.8%
v1135954
9.8%
u1135954
9.8%
a1135954
9.8%
l1135954
9.8%
p41636
 
0.4%
J20818
 
0.2%
Other values (4)83272
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10348494
89.6%
Uppercase Letter1177590
 
10.2%
Space Separator20818
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2292726
22.2%
d2271908
22.0%
n1156772
11.2%
v1135954
11.0%
u1135954
11.0%
a1135954
11.0%
l1135954
11.0%
p41636
 
0.4%
o20818
 
0.2%
t20818
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
I1135954
96.5%
J20818
 
1.8%
A20818
 
1.8%
Space Separator
ValueCountFrequency (%)
20818
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11526084
99.8%
Common20818
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2292726
19.9%
d2271908
19.7%
n1156772
10.0%
I1135954
9.9%
v1135954
9.9%
u1135954
9.9%
a1135954
9.9%
l1135954
9.9%
p41636
 
0.4%
J20818
 
0.2%
Other values (3)62454
 
0.5%
Common
ValueCountFrequency (%)
20818
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11546902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2292726
19.9%
d2271908
19.7%
n1156772
10.0%
I1135954
9.8%
v1135954
9.8%
u1135954
9.8%
a1135954
9.8%
l1135954
9.8%
p41636
 
0.4%
J20818
 
0.2%
Other values (4)83272
 
0.7%

fico
Real number (ℝ≥0)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.866188
Minimum662
Maximum847.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 MiB
2021-07-14T22:31:57.664871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum662
5-th percentile662
Q1672
median692
Q3712
95-th percentile762
Maximum847.5
Range185.5
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.63904905
Coefficient of variation (CV)0.04533684193
Kurtosis1.729464154
Mean697.866188
Median Absolute Deviation (MAD)20
Skewness1.299823685
Sum807272066
Variance1001.029425
MonotonicityNot monotonic
2021-07-14T22:31:57.789878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
662104693
 
9.1%
672101348
 
8.8%
667101243
 
8.8%
67790606
 
7.8%
68289374
 
7.7%
68778535
 
6.8%
69276498
 
6.6%
69768462
 
5.9%
70262542
 
5.4%
70756174
 
4.9%
Other values (28)327297
28.3%
ValueCountFrequency (%)
662104693
9.1%
667101243
8.8%
672101348
8.8%
67790606
7.8%
68289374
7.7%
68778535
6.8%
69276498
6.6%
69768462
5.9%
70262542
5.4%
70756174
4.9%
ValueCountFrequency (%)
847.5154
 
< 0.1%
842210
 
< 0.1%
837335
 
< 0.1%
832602
 
0.1%
827896
 
0.1%
8221168
 
0.1%
8171580
0.1%
8121997
0.2%
8072791
0.2%
8023219
0.3%

months_cr_line
Real number (ℝ≥0)

Distinct696
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.0901353
Minimum35
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-07-14T22:31:57.930439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile73
Q1133
median174
Q3236
95-th percentile358
Maximum999
Range964
Interquartile range (IQR)103

Descriptive statistics

Standard deviation86.59279216
Coefficient of variation (CV)0.453151556
Kurtosis1.532438983
Mean191.0901353
Median Absolute Deviation (MAD)49
Skewness1.027122193
Sum221047718
Variance7498.311654
MonotonicityNot monotonic
2021-07-14T22:31:58.081526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14410226
 
0.9%
1579422
 
0.8%
1559387
 
0.8%
1418795
 
0.8%
1308765
 
0.8%
1468759
 
0.8%
1648677
 
0.8%
1328664
 
0.7%
1538647
 
0.7%
1518482
 
0.7%
Other values (686)1066948
92.2%
ValueCountFrequency (%)
355
 
< 0.1%
36300
 
< 0.1%
37675
0.1%
381560
0.1%
391175
0.1%
401321
0.1%
411530
0.1%
421396
0.1%
431448
0.1%
441193
0.1%
ValueCountFrequency (%)
9991
< 0.1%
8521
< 0.1%
8511
< 0.1%
8411
< 0.1%
8211
< 0.1%
8191
< 0.1%
8031
< 0.1%
7931
< 0.1%
7861
< 0.1%
7851
< 0.1%

ROI
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1084282
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.321193
Minimum-40000
Maximum28296.87792
Zeros111
Zeros (%)< 0.1%
Negative209537
Negative (%)18.1%
Memory size8.8 MiB
2021-07-14T22:31:58.229315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-40000
5-th percentile-10475.19
Q1268.7526887
median1111.058756
Q32408.416563
95-th percentile6537.777399
Maximum28296.87792
Range68296.87792
Interquartile range (IQR)2139.663874

Descriptive statistics

Standard deviation5283.968512
Coefficient of variation (CV)13.23237686
Kurtosis7.550560969
Mean399.321193
Median Absolute Deviation (MAD)1005.588309
Skewness-1.919955349
Sum461923575.1
Variance27920323.24
MonotonicityNot monotonic
2021-07-14T22:31:58.354287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
838.3548364174
 
< 0.1%
1258.436374167
 
< 0.1%
1510.128586151
 
< 0.1%
1257.527562151
 
< 0.1%
1887.644562126
 
< 0.1%
1006.027804123
 
< 0.1%
2095.911784118
 
< 0.1%
2516.862749116
 
< 0.1%
0111
 
< 0.1%
1676.719673109
 
< 0.1%
Other values (1084272)1155426
99.9%
ValueCountFrequency (%)
-4000023
< 0.1%
-399701
 
< 0.1%
-398001
 
< 0.1%
-39308.451
 
< 0.1%
-39217.661
 
< 0.1%
-392001
 
< 0.1%
-39182.071
 
< 0.1%
-39151.971
 
< 0.1%
-39137.061
 
< 0.1%
-39123.81
 
< 0.1%
ValueCountFrequency (%)
28296.877921
< 0.1%
27862.506731
< 0.1%
27837.639691
< 0.1%
27760.913881
< 0.1%
27687.030891
< 0.1%
27686.388171
< 0.1%
27670.382321
< 0.1%
27663.618341
< 0.1%
27663.61831
< 0.1%
27451.008161
< 0.1%

Interactions

2021-07-14T22:29:13.571915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:14.164761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:14.686834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:15.248196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:15.730259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:16.264828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:16.714787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:17.231400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:17.763846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:18.249167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:18.731365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:19.298209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:19.814006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:20.344778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:20.964725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:21.546008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:22.250703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:22.881822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:23.459330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:24.072795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:24.723455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:25.248264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:25.731410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:26.233164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:26.679628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:27.279999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:27.731358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:28.265068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:28.781341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:29.329392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:29.848184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:30.448310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:30.989342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:31.514671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:32.062088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:32.595513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:33.099722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:33.631500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:34.128075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:34.690653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:35.197450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:35.714637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:36.381382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:36.866685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:37.379682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:37.897404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:38.464660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:38.998413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:39.533500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:40.149481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:40.780275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:41.315008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:41.829727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:42.347343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:42.881476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:43.429005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:43.915067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:44.426193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:44.912868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:45.459851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:45.915066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:46.448436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:46.948041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:47.498414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:48.014698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:48.551790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:49.265530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:49.775135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:50.280814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:50.796930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:29:51.305568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-14T22:31:35.577515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-14T22:31:36.871307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:37.527551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:38.164495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:38.708548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:39.214876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:39.714657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:40.181425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:40.631538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:41.164989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:41.595246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:42.065693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:42.582425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:43.100235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:43.618160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:44.151244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:44.668893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-14T22:31:45.155259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-14T22:31:58.598360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-14T22:31:58.825227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-14T22:31:59.071789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-14T22:31:59.311930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-14T22:31:59.546252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-14T22:31:45.734665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-14T22:31:47.798071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

loan_amntterminstallmentgradeemp_lengthhome_ownershipannual_incverification_statusloan_statuspurposeaddr_statedtidelinq_2yrsinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymntapplication_typeficomonths_cr_lineROI
03600.036 months123.03310.0MORTGAGE55000.0Not Verified1debt_consolidationPA5.910.01.07.00.02765.029.713.04421.723917Individual677.0148821.723917
124700.036 months820.28310.0MORTGAGE65000.0Not Verified1small_businessSD16.061.04.022.00.021470.019.238.025679.660000Individual717.0192979.660000
220000.060 months432.66210.0MORTGAGE63000.0Not Verified1home_improvementIL10.780.00.06.00.07869.056.218.022705.924294Joint App697.01832705.924294
310400.060 months289.9163.0MORTGAGE104433.0Source Verified1major_purchasePA25.371.03.012.00.021929.064.535.011740.500000Individual697.02101340.500000
411950.036 months405.1834.0RENT34000.0Source Verified1debt_consolidationGA10.200.00.05.00.08822.068.46.013708.948530Individual692.03381758.948530
520000.036 months637.58210.0MORTGAGE180000.0Not Verified1debt_consolidationMN14.670.00.012.00.087329.084.527.021393.800000Individual682.03061393.800000
620000.036 months631.26210.0MORTGAGE85000.0Not Verified1major_purchaseSC17.611.00.08.00.0826.05.715.021538.508977Individual707.02011538.508977
710000.036 months306.4516.0RENT85000.0Not Verified1credit_cardPA13.070.01.014.01.010464.034.523.010998.971575Individual687.0164998.971575
88000.036 months263.74210.0MORTGAGE42000.0Not Verified1credit_cardRI34.800.00.08.00.07034.039.118.08939.580503Individual702.0252939.580503
91400.036 months47.1033.0MORTGAGE64000.0Not Verified1otherNC34.950.00.017.00.037828.067.224.01575.160698Individual702.0233175.160698

Last rows

loan_amntterminstallmentgradeemp_lengthhome_ownershipannual_incverification_statusloan_statuspurposeaddr_statedtidelinq_2yrsinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymntapplication_typeficomonths_cr_lineROI
115676216000.060 months347.80210.0MORTGAGE120000.0Not Verified1debt_consolidationNY12.401.02.021.01.05995.07.634.018183.147643Individual747.01502183.147643
115676311200.060 months257.6635.0RENT86000.0Source Verified0debt_consolidationWA4.800.00.07.02.012581.054.714.06182.920000Individual667.0270-5017.080000
115676415000.036 months487.4728.0MORTGAGE60000.0Not Verified1debt_consolidationCO26.400.00.024.00.048654.036.142.015908.001163Individual682.0432908.001163
115676536400.060 months856.2435.0RENT95000.0Verified0credit_cardCA21.500.00.011.00.055723.079.824.02539.420000Individual722.0355-33860.580000
115676623800.060 months559.85310.0MORTGAGE119000.0Not Verified1debt_consolidationOH32.730.01.013.00.0107747.089.529.029818.871195Individual687.02906018.871195
115676718000.060 months377.9525.0OWN130000.0Not Verified1home_improvementTX20.590.01.017.00.023833.034.039.020756.233632Individual737.01472756.233632
115676829400.060 months683.9439.0MORTGAGE180792.0Not Verified1debt_consolidationCA22.030.01.016.00.077480.085.232.035848.764532Individual707.01756448.764532
115676932000.060 months752.7433.0MORTGAGE157000.0Source Verified0home_improvementAZ10.340.00.014.00.0111598.027.418.03737.940000Individual737.064-28262.060000
115677016000.060 months362.34310.0RENT150000.0Not Verified1medicalNC12.250.00.012.04.07700.055.028.018660.607569Individual667.02302660.607569
115677124000.060 months564.5636.0RENT110000.0Not Verified0debt_consolidationFL18.300.00.010.01.017641.068.131.06755.400000Individual662.0207-17244.600000